Application of machine learning models in groundwater quality assessment and prediction: progress and challenges

IF 6.1 2区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL
Yanpeng Huang, Chao Wang, Yuanhao Wang, Guangfeng Lyu, Sijie Lin, Weijiang Liu, Haobo Niu, Qing Hu
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Abstract

Groundwater quality assessment and prediction (GQAP) is vital for protecting groundwater resources. Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally demanding. Recently, the application of machine learning (ML) in GAQP (GQAPxML) has been widely studied due to ML’s reliability and efficiency. While many GQAPxML publications exist, a thorough review is missing. This review provides a comprehensive summary of the development of ML applications in the field of GQAP. First, the workflow of ML modeling is briefly introduced, as are data preparation, model development, model evaluation, and model application. Second, 299 publications related to the topic are filtered, mainly through ML modeling. Subsequently, many aspects of GQAPxML, such as publication trends, the spatial distribution of study areas, the size of data sets, and ML algorithms, are discussed from a bibliometric perspective. In addition, we review in detail the well-established applications and recent findings for several subtopics, including groundwater quality assessment, groundwater quality modeling using groundwater quality parameters, groundwater quality spatial mapping, probability estimation of exceeding the groundwater quality threshold, groundwater quality temporal prediction, and the hybrid use of ML and physics-based models. Finally, the development of GQAPxML is explored from three perspectives: data collection and preprocessing, model building and evaluation, and the broadening of model applications. This review provides a reference for environmental scientists to better understand GQAPxML and promotes the development of innovative methods and improvements in modeling quality.

Abstract Image

机器学习模型在地下水质量评估和预测中的应用:进展与挑战
地下水质量评估和预测(GQAP)对保护地下水资源至关重要。传统的 GQAP 方法无法充分捕捉属性之间的复杂关系,而且存在计算量大的缺点。最近,由于机器学习(ML)的可靠性和高效性,机器学习在 GQAP(GQAPxML)中的应用得到了广泛研究。虽然已有许多关于 GQAPxML 的出版物,但还缺少一份全面的综述。本综述全面总结了 ML 在 GQAP 领域的应用发展情况。首先,简要介绍了 ML 建模的工作流程,以及数据准备、模型开发、模型评估和模型应用。其次,主要通过 ML 建模筛选了与该主题相关的 299 篇出版物。随后,从文献计量学的角度讨论了 GQAPxML 的许多方面,如发表趋势、研究区域的空间分布、数据集的规模以及 ML 算法。此外,我们还详细回顾了几个子课题的成熟应用和最新研究成果,包括地下水水质评估、使用地下水水质参数的地下水水质建模、地下水水质空间绘图、超过地下水水质阈值的概率估计、地下水水质时间预测以及 ML 和基于物理的模型的混合使用。最后,从数据收集和预处理、模型构建和评估以及拓宽模型应用三个方面探讨了 GQAPxML 的发展。这篇综述为环境科学家更好地理解 GQAPxML 提供了参考,并促进了创新方法的开发和建模质量的提高。
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来源期刊
Frontiers of Environmental Science & Engineering
Frontiers of Environmental Science & Engineering ENGINEERING, ENVIRONMENTAL-ENVIRONMENTAL SCIENCES
CiteScore
10.90
自引率
12.50%
发文量
988
审稿时长
6.1 months
期刊介绍: Frontiers of Environmental Science & Engineering (FESE) is an international journal for researchers interested in a wide range of environmental disciplines. The journal''s aim is to advance and disseminate knowledge in all main branches of environmental science & engineering. The journal emphasizes papers in developing fields, as well as papers showing the interaction between environmental disciplines and other disciplines. FESE is a bi-monthly journal. Its peer-reviewed contents consist of a broad blend of reviews, research papers, policy analyses, short communications, and opinions. Nonscheduled “special issue” and "hot topic", including a review article followed by a couple of related research articles, are organized to publish novel contributions and breaking results on all aspects of environmental field.
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